{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "介绍如何在pytorch环境下，使用FGSM算法攻击基于ImageNet数据集预训练的alexnet模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter notebook中使用Anaconda中的环境需要单独配置，默认情况下使用的是系统默认的Python环境，以使用advbox环境为例。\n",
    "首先在默认系统环境下执行以下命令，安装ipykernel。\n",
    "\n",
    "    conda install ipykernel\n",
    "    conda install -n advbox ipykernel\n",
    "\n",
    "在advbox环境下激活，这样启动后就可以在界面上看到advbox了。\n",
    "\n",
    "    python -m ipykernel install --user --name advbox --display-name advbox \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.basicConfig(level=logging.INFO,format=\"%(filename)s[line:%(lineno)d] %(levelname)s %(message)s\")\n",
    "logger=logging.getLogger(__name__)\n",
    "\n",
    "import torch\n",
    "import torchvision\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n",
    "import torch.utils.data.dataloader as Data\n",
    "import torch.nn as nn\n",
    "from torchvision import models\n",
    "from adversarialbox.adversary import Adversary\n",
    "from adversarialbox.attacks.gradient_method import FGSMT\n",
    "from adversarialbox.attacks.gradient_method import FGSM\n",
    "from adversarialbox.models.pytorch import PytorchModel\n",
    "import numpy as np\n",
    "import cv2\n",
    "from tools import show_images_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#定义被攻击的图片\n",
    "image_path=\"tutorials/cropped_panda.jpg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-3-cb761f677857>[line:2] INFO CUDA Available: False\n",
      "pytorch.py[line:66] INFO Finish PytorchModel init\n",
      "base.py[line:87] INFO adversary:\n",
      "         original_label: 388\n",
      "         target_label: 538\n",
      "         is_targeted_attack: True\n",
      "gradient_method.py[line:86] INFO epsilons=0.001,epsilons_max=0.5,steps=100,epsilon_steps=1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n",
      "(1, 3, 224, 224)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "gradient_method.py[line:133] INFO step=1, epsilon = 0.00100, pre_label = 388, adv_label=388 logits=12.8916740417\n",
      "gradient_method.py[line:133] INFO step=2, epsilon = 0.00100, pre_label = 388, adv_label=388 logits=11.9266300201\n",
      "gradient_method.py[line:133] INFO step=3, epsilon = 0.00100, pre_label = 388, adv_label=388 logits=11.0248250961\n",
      "gradient_method.py[line:133] INFO step=4, epsilon = 0.00100, pre_label = 388, adv_label=388 logits=10.17000103\n",
      "gradient_method.py[line:133] INFO step=5, epsilon = 0.00100, pre_label = 388, adv_label=388 logits=9.42082595825\n",
      "gradient_method.py[line:133] INFO step=6, epsilon = 0.00100, pre_label = 388, adv_label=388 logits=8.80765151978\n",
      "gradient_method.py[line:133] INFO step=7, epsilon = 0.00100, pre_label = 388, adv_label=388 logits=8.29833221436\n",
      "gradient_method.py[line:133] INFO step=8, epsilon = 0.00100, pre_label = 388, adv_label=388 logits=7.83008337021\n",
      "gradient_method.py[line:133] INFO step=9, epsilon = 0.00100, pre_label = 388, adv_label=388 logits=7.41248893738\n",
      "gradient_method.py[line:133] INFO step=10, epsilon = 0.00100, pre_label = 388, adv_label=388 logits=7.0526509285\n",
      "gradient_method.py[line:133] INFO step=11, epsilon = 0.00100, pre_label = 388, adv_label=388 logits=6.67402505875\n",
      "gradient_method.py[line:133] INFO step=12, epsilon = 0.00100, pre_label = 388, adv_label=388 logits=6.36769342422\n",
      "gradient_method.py[line:133] INFO step=13, epsilon = 0.00100, pre_label = 388, adv_label=388 logits=6.04203033447\n",
      "gradient_method.py[line:133] INFO step=14, epsilon = 0.00100, pre_label = 388, adv_label=153 logits=5.85269260406\n",
      "gradient_method.py[line:133] INFO step=15, epsilon = 0.00100, pre_label = 388, adv_label=153 logits=5.75052928925\n",
      "gradient_method.py[line:133] INFO step=16, epsilon = 0.00100, pre_label = 388, adv_label=153 logits=5.71771430969\n",
      "gradient_method.py[line:133] INFO step=17, epsilon = 0.00100, pre_label = 388, adv_label=153 logits=5.71007919312\n",
      "gradient_method.py[line:133] INFO step=18, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.64453125\n",
      "gradient_method.py[line:133] INFO step=19, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.62803030014\n",
      "gradient_method.py[line:133] INFO step=20, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.56733751297\n",
      "gradient_method.py[line:133] INFO step=21, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.57567405701\n",
      "gradient_method.py[line:133] INFO step=22, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.5092010498\n",
      "gradient_method.py[line:133] INFO step=23, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.45419359207\n",
      "gradient_method.py[line:133] INFO step=24, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.41299533844\n",
      "gradient_method.py[line:133] INFO step=25, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.38274097443\n",
      "gradient_method.py[line:133] INFO step=26, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.34302330017\n",
      "gradient_method.py[line:133] INFO step=27, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.34468078613\n",
      "gradient_method.py[line:133] INFO step=28, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.31183052063\n",
      "gradient_method.py[line:133] INFO step=29, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.36119651794\n",
      "gradient_method.py[line:133] INFO step=30, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.4518237114\n",
      "gradient_method.py[line:133] INFO step=31, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.53023147583\n",
      "gradient_method.py[line:133] INFO step=32, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.68286037445\n",
      "gradient_method.py[line:133] INFO step=33, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.7369556427\n",
      "gradient_method.py[line:133] INFO step=34, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.76184844971\n",
      "gradient_method.py[line:133] INFO step=35, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.8539981842\n",
      "gradient_method.py[line:133] INFO step=36, epsilon = 0.00100, pre_label = 388, adv_label=852 logits=5.88561534882\n",
      "gradient_method.py[line:133] INFO step=37, epsilon = 0.00100, pre_label = 388, adv_label=538 logits=6.2413187027\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attack success, adversarial_label=538\n",
      "fgsm attack done\n"
     ]
    }
   ],
   "source": [
    "# Define what device we are using\n",
    "logging.info(\"CUDA Available: {}\".format(torch.cuda.is_available()))\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "#cv2默认读取格式为bgr bgr -> rgb   \n",
    "orig = cv2.imread(image_path)[..., ::-1]\n",
    "#转换成224*224\n",
    "orig = cv2.resize(orig, (224, 224))\n",
    "img = orig.copy().astype(np.float32)\n",
    "\n",
    "#图像数据标准化\n",
    "mean = [0.485, 0.456, 0.406]\n",
    "std = [0.229, 0.224, 0.225]\n",
    "img /= 255.0\n",
    "img = (img - mean) / std\n",
    "\n",
    "#pytorch中图像格式为CHW  \n",
    "#[224,224,3]->[3,224,224]\n",
    "img = img.transpose(2, 0, 1)\n",
    "\n",
    "img = Variable(torch.from_numpy(img).to(device).float().unsqueeze(0)).cpu().numpy()\n",
    "\n",
    "\n",
    "# Initialize the network\n",
    "#Alexnet\n",
    "model = models.alexnet(pretrained=True).to(device).eval()\n",
    "\n",
    "#print(model)\n",
    "\n",
    "#设置为不保存梯度值 自然也无法修改\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# advbox demo\n",
    "m = PytorchModel(\n",
    "    model, None,(-3, 3),\n",
    "    channel_axis=1)\n",
    "attack = FGSMT(m)\n",
    "\n",
    "# 静态epsilons\n",
    "attack_config = {\"epsilons\": 0.001, \"epsilon_steps\": 1, \"steps\": 100}\n",
    "\n",
    "inputs=img\n",
    "labels = None\n",
    "\n",
    "print(inputs.shape)\n",
    "\n",
    "adversary = Adversary(inputs, labels)\n",
    "\n",
    "tlabel = 538\n",
    "adversary.set_target(is_targeted_attack=True, target_label=tlabel)\n",
    "\n",
    "\n",
    "adversary = attack(adversary, **attack_config)\n",
    "\n",
    "\n",
    "if adversary.is_successful():\n",
    "    print(\n",
    "        'attack success, adversarial_label=%d'\n",
    "        % (adversary.adversarial_label))\n",
    "\n",
    "    adv=adversary.adversarial_example[0]\n",
    "\n",
    "else:\n",
    "    print('attack failed')\n",
    "\n",
    "\n",
    "print(\"fgsm attack done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#格式转换\n",
    "adv = adv.transpose(1, 2, 0)\n",
    "adv = (adv * std) + mean\n",
    "adv = adv * 256.0\n",
    "adv = np.clip(adv, 0, 255).astype(np.uint8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l0=112686 l2=62210.643382\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#显示原始图片  抵抗样本 以及两张图之间的差异  其中灰色代表没有差异的像素点\n",
    "show_images_diff(orig,adversary.original_label,adv,adversary.adversarial_label)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "advbox",
   "language": "python",
   "name": "advbox"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
